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Found 5 papers, 2 papers with code
Contrastive Learning with Consistent Representations
Contrastive learning demonstrates great promise for representation learning.
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
On mainstream DNN training tasks, BlueFog reaches a much higher throughput and achieves an overall $1. 2\times \sim 1. 8\times$ speedup over Horovod, a state-of-the-art distributed deep learning package based on Ring-Allreduce.
Exponential Graph is Provably Efficient for Decentralized Deep Training
Experimental results on a variety of tasks and models demonstrate that decentralized (momentum) SGD over exponential graphs promises both fast and high-quality training.
Communicate Then Adapt: An Effective Decentralized Adaptive Method for Deep Training
Decentralized adaptive gradient methods, in which each node averages only with its neighbors, are critical to save communication and wall-clock training time in deep learning tasks.
Global Adversarial Attacks for Assessing Deep Learning Robustness
It has been shown that deep neural networks (DNNs) may be vulnerable to adversarial attacks, raising the concern on their robustness particularly for safety-critical applications.
